Everything You Always Wanted to Know About SEM (Structural Equations Modeling) But Were Afraid to Ask

By Dawn Iacobucci. 2009

296 citations recorded by the source corpus.

Subfields: Information Systems and Management, Marketing, Organizational Behavior and Human Resource Management

View Paper: https://www.semanticscholar.org/paper/39226123120e6bed3df274e62bdb43a25bd0eb62

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Information Systems and Management

1. Kuttimani Tamilmani
2. Nripendra P. Rana
3. Pushp P. Patil
4. J. Pulido‐Fernández
5. P. Cárdenas‐García
6. Vishnupriya Raghavan
7. Richard P. Bagozzi
8. Jeff J. Kosovich

In Marketing

1. J. Pulido‐Fernández
2. P. Cárdenas‐García
3. Richard P. Bagozzi
4. Nripendra P. Rana
5. Vishnupriya Raghavan
6. Jeff J. Kosovich
7. S. Getty
8. Kuttimani Tamilmani

In Organizational Behavior and Human Resource Management

1. Kuttimani Tamilmani
2. Pushp P. Patil
3. J. Pulido‐Fernández
4. P. Cárdenas‐García
5. Vishnupriya Raghavan
6. Richard P. Bagozzi
7. Nripendra P. Rana
8. Jeff J. Kosovich

Citations in the corpus

• Analyzing the Bidirectional Relationship between Tourism Growth and Economic Development (2020, methodology): “Although there are different methods of statistical modeling, depending on the objective of the analysis and the characteristics of the variables to be analyzed, structural equation model (SEM) is an appropriate one to perform the analysis and address the objectives of the study: in the first part, ”
• Structural equation models are modelling tools with many ambiguities: Comments acknowledging the need for caution and humility in their use (2010, background): “Everything You Always Wanted to Know… Motivation for SEMs Iacobucci points out that SEMs take into account measurement error and that regression of a dependent variable on a factor measured by, say, 3 indicators overcomes problems E-mail address: bagozzi@umich.edu.”
• Does concrete language in disclosures increase willingness to invest? (2014, unknown): “Fit statistics indicate that the model is a good fit for the data: v2ð1 df Þ = 0.112 (p = 0.738), RMSEA\ 0.001, and CFI = 1.000 (Iacobucci 2009, 2010; Kline 2011). geographically distant (Houston-based) firm in our materials did not feel psychologically close for our participants.”
• The Median Split: Robust, Refined, and Revived (2015, background): “In Iacobucci et al. (2015), we reviewed the literature on why median splits are often attractive to researchers, and wanted to catalog their popularity to frame the importance of furthering our knowledge and sophistication regarding when median splits are, and are not, appropriate.”
• Understanding consumer adoption of mobile payment in India: Extending Meta-UTAUT model with personal innovativeness, anxiety, trust, and grievance redressal (2020, methodology): “In comparison, first generation statistical techniques such as linear regression can only test relationships with one dependent variable at a time, yielding less than accurate results for mediated relationships (Iacobucci, 2009).”
• A structural equation model of predictors of online learners’ engagement and satisfaction (2019, background): “In SEM studies, variables are classified as either exogenous, which are like independent variables, or endogenous, which are dependent, intermediate, or outcome variables (Hoyle, 1995; Iacobucci, 2009; Kline, 2011).”
• A Practical Measure of Student Motivation (2015, methodology): “Instead of using strict accept/reject decision rules (Iacobucci, 2009; Steiger, 2007), we used assessments of global model fit, local model fit, and change in model fit between competing models to make holistic judgments about the adequacy of tested models.”